Minimum Time Learning Model Predictive Control

TL;DR

This paper introduces a Learning Model Predictive Control approach that ensures finite-time convergence and safety for linear and nonlinear systems, with applications demonstrated in obstacle avoidance and racing scenarios.

Contribution

It develops a convexified LMPC method with guaranteed convergence, safety, and non-decreasing performance for time optimal control problems.

Findings

Guarantees finite-time convergence for LMPC.

Ensures recursive constraint satisfaction.

Demonstrates effectiveness in obstacle avoidance and racing tasks.

Abstract

In this paper we present a Learning Model Predictive Control (LMPC) strategy for linear and nonlinear time optimal control problems. Our work builds on existing LMPC methodologies and it guarantees finite time convergence properties for the closed-loop system. We show how to construct a time varying safe set and terminal cost function using closed-loop data. The resulting LMPC policy is time varying and it guarantees recursive constraint satisfaction and non-decreasing performance. Computational efficiency is obtained by convexifing the safe set and terminal cost function. We demonstrate that, for a class of nonlinear system and convex constraints, the convex LMPC formulation guarantees recursive constraint satisfaction and non-decreasing performance. Finally, we illustrate the effectiveness of the proposed strategies on minimum time obstacle avoidance and racing examples.